{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amortization Example in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code to support article at [Practical Business Python](http://pbpython.com/amortization-model.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup the imports and matplotlib plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to build an amortization table/schedule and return the value as a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def amortization_table(interest_rate, years, payments_year, principal, addl_principal=0, start_date=date.today()):\n",
    "    \"\"\" Calculate the amortization schedule given the loan details\n",
    "    \n",
    "     Args:\n",
    "        interest_rate: The annual interest rate for this loan\n",
    "        years: Number of years for the loan\n",
    "        payments_year: Number of payments in a year\n",
    "        principal: Amount borrowed\n",
    "        addl_principal (optional): Additional payments to be made each period. Assume 0 if nothing provided.\n",
    "                                   must be a value less then 0, the function will convert a positive value to\n",
    "                                   negative\n",
    "        start_date (optional): Start date. Will start on first of next month if none provided\n",
    "\n",
    "    Returns:\n",
    "        schedule: Amortization schedule as a pandas dataframe\n",
    "        summary: Pandas dataframe that summarizes the payoff information\n",
    "    \"\"\"\n",
    "    # Ensure the additional payments are negative\n",
    "    if addl_principal > 0:\n",
    "        addl_principal = -addl_principal\n",
    "    \n",
    "    # Create an index of the payment dates\n",
    "    rng = pd.date_range(start_date, periods=years * payments_year, freq='MS')\n",
    "    rng.name = \"Payment_Date\"\n",
    "    \n",
    "    # Build up the Amortization schedule as a DataFrame\n",
    "    df = pd.DataFrame(index=rng,columns=['Payment', 'Principal', 'Interest', \n",
    "                                         'Addl_Principal', 'Curr_Balance'], dtype='float')\n",
    "    \n",
    "    # Add index by period (start at 1 not 0)\n",
    "    df.reset_index(inplace=True)\n",
    "    df.index += 1\n",
    "    df.index.name = \"Period\"\n",
    "    \n",
    "    # Calculate the payment, principal and interests amounts using built in Numpy functions\n",
    "    per_payment = np.pmt(interest_rate/payments_year, years*payments_year, principal)\n",
    "    df[\"Payment\"] = per_payment\n",
    "    df[\"Principal\"] = np.ppmt(interest_rate/payments_year, df.index, years*payments_year, principal)\n",
    "    df[\"Interest\"] = np.ipmt(interest_rate/payments_year, df.index, years*payments_year, principal)\n",
    "        \n",
    "    # Round the values\n",
    "    df = df.round(2) \n",
    "    \n",
    "    # Add in the additional principal payments\n",
    "    df[\"Addl_Principal\"] = addl_principal\n",
    "    \n",
    "    # Store the Cumulative Principal Payments and ensure it never gets larger than the original principal\n",
    "    df[\"Cumulative_Principal\"] = (df[\"Principal\"] + df[\"Addl_Principal\"]).cumsum()\n",
    "    df[\"Cumulative_Principal\"] = df[\"Cumulative_Principal\"].clip(lower=-principal)\n",
    "    \n",
    "    # Calculate the current balance for each period\n",
    "    df[\"Curr_Balance\"] = principal + df[\"Cumulative_Principal\"]\n",
    "    \n",
    "    # Determine the last payment date\n",
    "    try:\n",
    "        last_payment = df.query(\"Curr_Balance <= 0\")[\"Curr_Balance\"].idxmax(axis=1, skipna=True)\n",
    "    except ValueError:\n",
    "        last_payment = df.last_valid_index()\n",
    "    \n",
    "    last_payment_date = \"{:%m-%d-%Y}\".format(df.loc[last_payment, \"Payment_Date\"])\n",
    "        \n",
    "    # Truncate the data frame if we have additional principal payments:\n",
    "    if addl_principal != 0:\n",
    "                \n",
    "        # Remove the extra payment periods\n",
    "        df = df.ix[0:last_payment].copy()\n",
    "        \n",
    "        # Calculate the principal for the last row\n",
    "        df.ix[last_payment, \"Principal\"] = -(df.ix[last_payment-1, \"Curr_Balance\"])\n",
    "        \n",
    "        # Calculate the total payment for the last row\n",
    "        df.ix[last_payment, \"Payment\"] = df.ix[last_payment, [\"Principal\", \"Interest\"]].sum()\n",
    "        \n",
    "        # Zero out the additional principal\n",
    "        df.ix[last_payment, \"Addl_Principal\"] = 0\n",
    "        \n",
    "    # Get the payment info into a DataFrame in column order\n",
    "    payment_info = (df[[\"Payment\", \"Principal\", \"Addl_Principal\", \"Interest\"]]\n",
    "                    .sum().to_frame().T)\n",
    "       \n",
    "    # Format the Date DataFrame\n",
    "    payment_details = pd.DataFrame.from_items([('payoff_date', [last_payment_date]),\n",
    "                                               ('Interest Rate', [interest_rate]),\n",
    "                                               ('Number of years', [years])\n",
    "                                              ])\n",
    "    # Add a column showing how much we pay each period.\n",
    "    # Combine addl principal with principal for total payment\n",
    "    payment_details[\"Period_Payment\"] = round(per_payment, 2) + addl_principal\n",
    "    \n",
    "    payment_summary = pd.concat([payment_details, payment_info], axis=1)\n",
    "    return df, payment_summary\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of running the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "schedule1, stats1 = amortization_table(0.05, 30, 12, 100000, addl_principal=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the start and end of the table as well as the summary stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment_Date</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Curr_Balance</th>\n",
       "      <th>Cumulative_Principal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-120.15</td>\n",
       "      <td>-416.67</td>\n",
       "      <td>0</td>\n",
       "      <td>99879.85</td>\n",
       "      <td>-120.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-120.66</td>\n",
       "      <td>-416.17</td>\n",
       "      <td>0</td>\n",
       "      <td>99759.19</td>\n",
       "      <td>-240.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-121.16</td>\n",
       "      <td>-415.66</td>\n",
       "      <td>0</td>\n",
       "      <td>99638.03</td>\n",
       "      <td>-361.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-121.66</td>\n",
       "      <td>-415.16</td>\n",
       "      <td>0</td>\n",
       "      <td>99516.37</td>\n",
       "      <td>-483.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-122.17</td>\n",
       "      <td>-414.65</td>\n",
       "      <td>0</td>\n",
       "      <td>99394.20</td>\n",
       "      <td>-605.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Payment_Date  Payment  Principal  Interest  Addl_Principal  \\\n",
       "Period                                                              \n",
       "1        2016-12-01  -536.82    -120.15   -416.67               0   \n",
       "2        2017-01-01  -536.82    -120.66   -416.17               0   \n",
       "3        2017-02-01  -536.82    -121.16   -415.66               0   \n",
       "4        2017-03-01  -536.82    -121.66   -415.16               0   \n",
       "5        2017-04-01  -536.82    -122.17   -414.65               0   \n",
       "\n",
       "        Curr_Balance  Cumulative_Principal  \n",
       "Period                                      \n",
       "1           99879.85               -120.15  \n",
       "2           99759.19               -240.81  \n",
       "3           99638.03               -361.97  \n",
       "4           99516.37               -483.63  \n",
       "5           99394.20               -605.80  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment_Date</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Curr_Balance</th>\n",
       "      <th>Cumulative_Principal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2046-07-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-525.78</td>\n",
       "      <td>-11.05</td>\n",
       "      <td>0</td>\n",
       "      <td>2125.09</td>\n",
       "      <td>-97874.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2046-08-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-527.97</td>\n",
       "      <td>-8.85</td>\n",
       "      <td>0</td>\n",
       "      <td>1597.12</td>\n",
       "      <td>-98402.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2046-09-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-530.17</td>\n",
       "      <td>-6.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1066.95</td>\n",
       "      <td>-98933.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2046-10-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-532.38</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>0</td>\n",
       "      <td>534.57</td>\n",
       "      <td>-99465.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2046-11-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-534.59</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-100000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Payment_Date  Payment  Principal  Interest  Addl_Principal  \\\n",
       "Period                                                              \n",
       "356      2046-07-01  -536.82    -525.78    -11.05               0   \n",
       "357      2046-08-01  -536.82    -527.97     -8.85               0   \n",
       "358      2046-09-01  -536.82    -530.17     -6.65               0   \n",
       "359      2046-10-01  -536.82    -532.38     -4.45               0   \n",
       "360      2046-11-01  -536.82    -534.59     -2.23               0   \n",
       "\n",
       "        Curr_Balance  Cumulative_Principal  \n",
       "Period                                      \n",
       "356          2125.09             -97874.91  \n",
       "357          1597.12             -98402.88  \n",
       "358          1066.95             -98933.05  \n",
       "359           534.57             -99465.43  \n",
       "360             0.00            -100000.00  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>payoff_date</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Number of years</th>\n",
       "      <th>Period_Payment</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-01-2046</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-193255.2</td>\n",
       "      <td>-100000.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93255.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  payoff_date  Interest Rate  Number of years  Period_Payment   Payment  \\\n",
       "0  11-01-2046           0.05               30         -536.82 -193255.2   \n",
       "\n",
       "   Principal  Addl_Principal  Interest  \n",
       "0 -100000.02             0.0 -93255.69  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try running some other scenarios and combining them into a single DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "schedule2, stats2 = amortization_table(0.05, 30, 12, 100000, addl_principal=-200)\n",
    "schedule3, stats3 = amortization_table(0.04, 15, 12, 100000, addl_principal=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>payoff_date</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Number of years</th>\n",
       "      <th>Period_Payment</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-01-2046</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-193255.20</td>\n",
       "      <td>-100000.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93255.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04-01-2037</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>-736.82</td>\n",
       "      <td>-131689.78</td>\n",
       "      <td>-51200.00</td>\n",
       "      <td>-48800.0</td>\n",
       "      <td>-80490.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-01-2031</td>\n",
       "      <td>0.04</td>\n",
       "      <td>15</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-133144.20</td>\n",
       "      <td>-100000.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-33143.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  payoff_date  Interest Rate  Number of years  Period_Payment    Payment  \\\n",
       "0  11-01-2046           0.05               30         -536.82 -193255.20   \n",
       "1  04-01-2037           0.05               30         -736.82 -131689.78   \n",
       "2  11-01-2031           0.04               15         -739.69 -133144.20   \n",
       "\n",
       "   Principal  Addl_Principal  Interest  \n",
       "0 -100000.02             0.0 -93255.69  \n",
       "1  -51200.00        -48800.0 -80490.16  \n",
       "2 -100000.03             0.0 -33143.85  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine all the scenarios into 1 view\n",
    "pd.concat([stats1, stats2, stats3], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment_Date</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Curr_Balance</th>\n",
       "      <th>Cumulative_Principal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-406.35</td>\n",
       "      <td>-333.33</td>\n",
       "      <td>0</td>\n",
       "      <td>99593.65</td>\n",
       "      <td>-406.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-407.71</td>\n",
       "      <td>-331.98</td>\n",
       "      <td>0</td>\n",
       "      <td>99185.94</td>\n",
       "      <td>-814.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-409.07</td>\n",
       "      <td>-330.62</td>\n",
       "      <td>0</td>\n",
       "      <td>98776.87</td>\n",
       "      <td>-1223.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-410.43</td>\n",
       "      <td>-329.26</td>\n",
       "      <td>0</td>\n",
       "      <td>98366.44</td>\n",
       "      <td>-1633.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-411.80</td>\n",
       "      <td>-327.89</td>\n",
       "      <td>0</td>\n",
       "      <td>97954.64</td>\n",
       "      <td>-2045.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Payment_Date  Payment  Principal  Interest  Addl_Principal  \\\n",
       "Period                                                              \n",
       "1        2016-12-01  -739.69    -406.35   -333.33               0   \n",
       "2        2017-01-01  -739.69    -407.71   -331.98               0   \n",
       "3        2017-02-01  -739.69    -409.07   -330.62               0   \n",
       "4        2017-03-01  -739.69    -410.43   -329.26               0   \n",
       "5        2017-04-01  -739.69    -411.80   -327.89               0   \n",
       "\n",
       "        Curr_Balance  Cumulative_Principal  \n",
       "Period                                      \n",
       "1           99593.65               -406.35  \n",
       "2           99185.94               -814.06  \n",
       "3           98776.87              -1223.13  \n",
       "4           98366.44              -1633.56  \n",
       "5           97954.64              -2045.36  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qbUfM+U9hrn0VfSbP0aEJUSOkWAhRBcrjv+MZs5ZDQX7p/RlbPkebJY4OTYhq\nJaehhKgG6qr6qP+bgD70U+m0IZs2Ytz9ECqkvaNDE6JayJGFENVIXdsS48m5qEGjMF96AfP1OPTv\nJx0dlhBVJsVCiGqmlMLo2Rdj9krw8cN89lHMTRvl1JRwaVIshKghqq4Pxh33YTz2D/TOLZjPP4E+\n8KOjwxKiUqRYCFHDVNPmpaem+g/DXDGn9C7w06ccHZYQFSLFQogrQCmF0as/xuwVYDFK7wLf9oXM\naitchhQLIa4g5eNXOhX6xJnoLZ9jvvB39C8HHB2WEOWSYiGEA6hrW2L8/QVUrwGYsc9gfrga/ccf\njg5LiIuSYiGEgyjDwIgYhPHsUrCdwJz1KDp9j6PDEuKC5KY8IRxMXVUf9cDj6LRdmG+tQLVqj4q6\nD2Wt5+jQhLCTIwshnITq2K102pCr/DFnTsDc/m+01o4OSwhAioUQTkXV8ca4/V6MSc+i//0J5uIZ\n6ONHHB2WEFIshHBG6tqWGE8vRHUMw5z3BOana9HFxY4OS9RiUiyEcFLKYsG4eQTGtMXo/emYcx+X\ny2yFw0ixEMLJqQZXY0x+FtVvKObiGZgb3pZHuoorToqFEC5AKYVx400Yz8SVToP+3BT0oQxHhyVq\nESkWQrgQVT8QY8J01KCRmHGzMNe9iS6SowxR86p0n8WZM2d48cUX+eWXX1BK8fDDD9OoUSOWLFnC\niRMnCA4OJiYmBh8fHwASEhJISkrCYrEQHR1N586dAcjMzGTlypUUFRXRtWtXoqOjASguLmb58uVk\nZmZitVqJiYmhQYMGVctYCBenlEKF90O364L5z1Xof0zGiJ6IatHG0aEJN1alI4vXX3+drl27Ehsb\ny4IFC2jSpAnr16+nY8eOxMXFERoaSkJCAgCHDx8mOTmZ2NhYpk6dSnx8vP0a8vj4eMaNG0dcXBxH\njx5l7969ACQmJuLn58fSpUsZOnQoa9asqWK6QrgPVa8+xsNTUX+5C3PFc5jvv44ulClDRM2odLE4\nc+YM+/bto1+/fgBYLBZ8fHxITU2lb9++AERGRpKSkgJAamoqvXr1wmKxEBwcTKNGjcjIyODkyZPk\n5+cTEhICQEREhL1NSkqKva/w8HDS0tIqn6kQbkgphdH9Roxnl0FOFuacxyj+6QdHhyXcUKVPQ2Vl\nZWG1Wlm5ciWHDh2iRYsWREdHk5ubi7+/PwD+/v7k5uYCYLPZaN26tb19QEAANpsNi8VCYGCg/f3A\nwEBsNptLccWhAAAgAElEQVS9zdllhmHg6+tLXl4efn5+lQ1bCLekrPVQ457C/HoLp1+YChG3oIbc\njvKQGX1E9aj0kYVpmhw4cIBBgwYxf/586tSpw/r1689bTylVpQDPJVMfCHFpRo8IrHNfQmfuw5z3\nJProYUeHJNxEpb92BAQEEBgYSMuWLYHS00Tr16/H39+fkydP2v+uV6+eff3s7Gx7+5ycHAICAggI\nCCAnJ+e898+2OfvaNE3y8/MveFSRnp5Oenq6/XVUVBRWq7WyqZXLy8urRvt3NMnPtXl5BaKmL6Lw\ny48pWPB36oy8B69bRqEM97j40f33n2PzW7t2rf3n0NBQQkNDgSoUC39/fwIDAzly5AiNGzcmLS2N\npk2b0rRpUzZt2sSIESPYtGkTYWFhAISFhbF06VKGDRuGzWbj2LFjhISEoJTCx8eHjIwMWrZsyZYt\nWxg8eLC9zebNm2nVqhXJycl06NDhgrGcm9BZp07V3GMrrVZrjfbvaJKfa7NareTl5UF4P1SLNuS/\ntoT8nVswxk5GBQY5Orwqqw37z1H5Wa1WoqKiLrhM6Sqc2zl48CAvvfQSxcXFXH311YwfPx7TNImN\njSU7O5ugoCBiYmLw9fUFSi+dTUxMxMPD47xLZ1esWGG/dHbs2LEAFBUVsWzZMg4ePIjVamXSpEkE\nBwdfVmxHjtTc5Gvyj9W11bb8tFmC/nw9+l8JqNvHom7oX62nh6+02rb/rqTGjRtfdFmVioUzk2JR\neZKfa7tYfvqXA5ivxUJQQ4z/m4Dyu8oB0VVdbd1/V8KlioV7nMQUQpRLNbsO4+lFqAZXY86ejP7+\nG0eHJFyIXFcnRC2iPD1RUfehQ6/HfC0WFd4PdevdKA9PR4cmnJwcWQhRC6nQrqWTEh75GXPeU+hj\nvzo6JOHkpFgIUUspa73SSQl7D8Cc/xTmti/kXiZxUXIaSohaTCmF6jcU3boD5isL0d/uwrhnAspX\nZkkQZcmRhRAC1eRajGmLUP6BmLMnoX/41tEhCScjxUIIAYDy9MK48wGMv47HfGUB5sfvos0SR4cl\nnIQUCyFEGapjN4zpi9H7/oO55Fl07m+ODkk4ASkWQojzKP9AjMf+gWrZFnNOjNyTIWSAWwhxYcpi\nQd06Bt0qFPPVWFSfm1F/uQNlWBwdmnAAObIQQlySat+l9LTUj+mYi59Bn8wpv5FwO1IshBDlUv4B\nGI/NRrXugDnnMXT6HkeHJK4wOQ0lhLgsyrCght+FbtW+dKqQXgNRw+9CWeS0VG0gRxZCiApR7Tpj\nzIhFH/gBM/YZ9O8nHR2SuAKkWAghKkxdVR9j8rP/vVrqMfRP+xwdkqhhUiyEEJWiDAvGyHsw7n4I\nc8VzmEmfytxSbkzGLIQQVaK69MRo3Axz5VzI/AH+Oh5Vx9vRYYlqJkcWQogqU8GNMaYuBMCc+wT6\neM09qVI4hhQLIUS1UHXqoO6NQfUdjDn/KfTeHY4OSVQjOQ0lhKg2pVOeD0Ff0wLz5RdQmT+gbv2r\nXF7rBuTIQghR7VTLthjTY9EHMzCXzkKfPuXokEQVSbEQQtQIZa2HMelZVJNrMZ+bgv71Z0eHJKpA\nioUQosYoiwUj6j7UsDsxFz6N3iPjGK5KxiyEEDXO6NUf3agZ5qq5qMMHUUOjUIZ8V3UlsreEEFeE\nuq4VxtML0em7MV+chy444+iQRAVIsRBCXDHKPwBjynMoXyvmvKfQJ445OiRxmaRYCCGuKOXpifq/\nCaiIQaU38H2319EhicsgYxZCiCtOKYXqPwzd+BrMVxaiBo9GDRiOUsrRoYmLkCMLIYTDqLadMP7+\nAnrbl+g1K9HFxY4OSVyEFAshhEOpoIYYT81H/5bz3xv48hwdkrgAKRZCCIdTdX0wJkwrvYFv7hPo\nLJmI0NlIsRBCOAVlWDDuuB81cDjm/L+jf/jW0SGJc0ixEEI4FSNyMMZ9MZgvzcf86ktHhyP+S4qF\nEMLpqPZdMZ6Yi/50LeaHq9Gm6eiQaj0pFkIIp6QaNcWYuhD90/eld3z/UeDokGo1KRZCCKelrFdh\nxPwD5V0Xc+E09O+/OTqkWqvKN+WZpsnUqVMJCAjgqaeeIi8vjyVLlnDixAmCg4OJiYnBx8cHgISE\nBJKSkrBYLERHR9O5c2cAMjMzWblyJUVFRXTt2pXo6GgAiouLWb58OZmZmVitVmJiYmjQoEFVQxZC\nuBDl6QljJ8OGdzDnPUXJ1Plgre/osGqdKh9ZbNy4kSZNmthfr1+/no4dOxIXF0doaCgJCQkAHD58\nmOTkZGJjY5k6dSrx8fForQGIj49n3LhxxMXFcfToUfbuLb39PzExET8/P5YuXcrQoUNZs2ZNVcMV\nQrggpRTGrXejBt9G3qzJ6IzvHB1SrVOlYpGTk8OePXsYMGCA/b3U1FT69u0LQGRkJCkpKfb3e/Xq\nhcViITg4mEaNGpGRkcHJkyfJz88nJCQEgIiICHublJQUe1/h4eGkpaVVJVwhhIsz+tyMz/i/Y66c\ni971laPDqVWqVCxWr17NPffcU2Y+l9zcXPz9/QHw9/cnNzcXAJvNVuYUUkBAADabDZvNRmBgoP39\nwMBAbDabvc3ZZYZh4OvrS16e3N0pRG3m2bkHxuRnMd+Nx/zXevsZClGzKl0sdu/eTb169WjevPkl\nd1Z1Tgwm/yiEEADqmpalc0p99SX6vXi0WeLokNxepQe49+3bR2pqKnv27KGwsJD8/HyWLVuGv78/\nJ0+etP9dr149oPRIIjs7294+JyeHgIAAAgICyMnJOe/9s23OvjZNk/z8fPz8/M6LJT09nfT0dPvr\nqKgorFZrZVMrl5eXV43272iSn2urNflZrZj/WMGZxc+g4hfjM+FpVB1vR4dXZY7ef2vXrrX/HBoa\nSmhoKFCFYnH33Xdz9913A/Ddd9/x8ccf8+ijj7JmzRo2bdrEiBEj2LRpE2FhYQCEhYWxdOlShg0b\nhs1m49ixY4SEhKCUwsfHh4yMDFq2bMmWLVsYPHiwvc3mzZtp1aoVycnJdOjQ4YKxnJvQWadOnaps\nauWyWq012r+jSX6urbblpydMR69eRu7sGIxHZ6B8XbtQOnL/Wa1WoqKiLris2u+zGDFiBGlpaUya\nNIlvv/2WESNGANC0aVNuuOEGYmJimDt3Lvfff7/9FNV9993HqlWrmDRpEg0bNqRLly4A9O/fn99/\n/52JEyeyceNGe3ESQoizlIcnauxkVMt2pXNK2U44OiS3pLSbDgQcOVJzs1bWtm9u7kbyc22Xys/8\nPAGd+AnG5GdRjZpd4ciqhyP3X+PGjS+6TO7gFkK4DWPQSNStY0rv9v5pn6PDcStSLIQQbsXo1R8j\neiLm8jnotF2ODsdtSLEQQrgd1TEMY8J0zDfiMHckOToctyDFQgjhllTLthhT5qAT3sL813pHh+Py\npFgIIdyWanxN6fO9t/4L84PX5cbeKpBiIYRwayogCOOpeej96ei3Vsjd3pUkxUII4faU31UYj81G\nZx1FvxqLLi52dEguR4qFEKJWUN4+GBOfQeefKX3yXlGho0NyKVIshBC1hvKqgzH+aZRXHcyls9EF\n+Y4OyWVIsRBC1CrKwwN1/2OooIaYsc+gT8tjDy6HFAshRK2jDAvqnkdQLdr+99neJx0dktOTYiGE\nqJWUUqioe1FdwzFfmCoTEJZDioUQotZSSmEMvwsVMai0YGTV3ASkrk6KhRCi1jNuHoEacjvmwuno\nY4cdHY5TkmIhhBCAETGodMbaRdPRR352dDhOR4qFEEL8l9F7AGr03zAXP4M+fNDR4TgVKRZCCHEO\nI7wf6o77MJfMRP+c6ehwnIYUCyGE+BOjex+Mux7CjHsWfSjD0eE4BSkWQghxAapbL4x7xmPGzUIf\n2O/ocBxOioUQQlyE6hKO8beJmMv+gc743tHhOJQUCyGEuATVuTvGvZMxVz6P3p/u6HAcRoqFEEKU\nQ3XohnH/lNLZan/8ztHhOIQUCyGEuAyqfReM+x/DXDUX/dM+R4dzxUmxEEKIy6Tad8UYOxlzxXO1\nbtBbioUQQlSA6tjtf4PeteiyWikWQghRQapzd4x7Him9rLaW3LgnxUIIISpBdQ3HGPNw6Y17tWBq\nECkWQghRSapbL9SdD2AuedbtJx+UYiGEEFVgdO+Dui269BGtR913enMpFkIIUUVGeCRq5D2Yi2eg\nj7vnA5SkWAghRDUweg1A/eXO0iMMN3xEqxQLIYSoJkbEIFT/YaXPw/j9N0eHU62kWAghRDUybh6B\n6hGBGTsTfTrP0eFUGykWQghRzdRf7kS161x6WW3BGUeHUy2kWAghRDVTSqFuvxfVtDnm8ufQhX84\nOqQqk2IhhBA1QCmF+uvDqHr1MV+cjy4udnRIVeJR2YY5OTksX76c3NxclFIMGDCAIUOGkJeXx5Il\nSzhx4gTBwcHExMTg4+MDQEJCAklJSVgsFqKjo+ncuTMAmZmZrFy5kqKiIrp27Up0dDQAxcXFLF++\nnMzMTKxWKzExMTRo0KDqWQshxBWgDAuMnYx+cR76tVi4/7HS91xQpY8sLBYLf/vb31i8eDHPPfcc\nn3/+Ob/++ivr16+nY8eOxMXFERoaSkJCAgCHDx8mOTmZ2NhYpk6dSnx8PFprAOLj4xk3bhxxcXEc\nPXqUvXv3ApCYmIifnx9Lly5l6NChrFmzphpSFkKIK0d5eGA89CT6VC76zRVo03R0SJVS6WLh7+9P\n8+bNAfD29qZJkybk5OSQmppK3759AYiMjCQlJQWA1NRUevXqhcViITg4mEaNGpGRkcHJkyfJz88n\nJCQEgIiICHublJQUe1/h4eGkpaVVOlEhhHAU5emF8cg09NFf0O+/bv+i7EqqZcwiKyuLQ4cO0bp1\na3Jzc/H39wdKC0pubi4ANputzCmkgIAAbDYbNpuNwMBA+/uBgYHYbDZ7m7PLDMPA19eXvDz3uRRN\nCFF7KO+6GBNnor/fi/7sQ0eHU2GVHrM4q6CggMWLFxMdHY23t/d5y5VSVd2E3cWqcXp6Ounp/3s2\nblRUFFartdq2+2deXl412r+jSX6uTfJzYlYr5rQF5M2ciFfQ1dTpN+S8VRyd39q1a+0/h4aGEhoa\nClSxWJSUlLBo0SIiIiLo3r07UHo0cfLkSfvf9erVA0qPJLKzs+1tc3JyCAgIICAggJycnPPeP9vm\n7GvTNMnPz8fPz++8OM5N6KxTp05VJbVLslqtNdq/o0l+rk3yc3Ke3jBxJvkLn+YPDy9Ul55lFjsy\nP6vVSlRU1AWXVek01KpVq2jatClDhvyvOnbr1o1NmzYBsGnTJsLCwgAICwtj+/btFBcXk5WVxbFj\nxwgJCcHf3x8fHx8yMjLQWrNlyxZ74QkLC2Pz5s0AJCcn06FDh6qEK4QQTkE1bILxyHTMN5ej96eX\n38AJKF3JkZZ9+/Yxc+ZMrrnmmtLriZXirrvuIiQkhNjYWLKzswkKCiImJgZfX1+g9NLZxMREPDw8\nzrt0dsWKFfZLZ8eOHQtAUVERy5Yt4+DBg1itViZNmkRwcPBlxXfkSM3N/Ojy32zKIfm5NsnPdejv\n9mDGL8Z4bDaq6XWAY/Nr3LjxRZdVulg4OykWlSf5uTbJz7WYKVvRa1/DeHIuKqih0xYLuYNbCCEc\nyOjeBzV4dOnT9n4/6ehwLkqKhRBCOJjRfxiqRx/MpbPR+c458aAUCyGEcAJq+N2oa1tyetEMdHGR\no8M5jxQLIYRwAkop1JhxqLo+6NXLnO4ubykWQgjhJJRhwWfCNHTWUfT6fzo6nDKkWAghhBNRdbwx\nJkxHp27F3PKZo8Oxk2IhhBBORlnrYUyaid7wDjot1dHhAFIshBDCKangxhgPT8V8bQn6UIajw5Fi\nIYQQzkq1bItxzyOYy+egs487NBYpFkII4cTU9TegbrkNM24W+rTj7lyXYiGEEE7OGDAM1SkMc8Vz\n6KJCx8TgkK0KIYSoEDU6GnVVffRrSxzyaFYpFkII4QKUYaDui0GftKET3rri25diIYQQLkJ5emGM\nfxq96yvMr768otuWYiGEEC5EWa/CePQZ9Ier0T98e8W2K8VCCCFcjGrUFOP+KZgvzUcfr7ln95xL\nioUQQrgg1b4LasQYzGX/uCKX1EqxEEIIF2VE3FJ6Se2qeeji4prdVo32LoQQokap26LBuy767Rdr\ndFpzKRZCCOHClGHBuH8K+sCP6C/W19h2pFgIIYSLU951MR6djv7iI/TeHTWyDSkWQgjhBlRAEMb4\naZirl6N//qna+5diIYQQbkJd1wrjrw+XziGV+1u19i3FQggh3Ijq1hvVeyDmqrnooqJq61eKhRBC\nuBk17E64yh/9zkvVdoWUFAshhHAzyjAw7o1BZ/6A3rSxWvqUYiGEEG5IedfFeGQa+pP30Pv+U+X+\npFgIIYSbUkENS+eQemUh+sSxKvUlxUIIIdyYatcZNSSq9AqpgvxK9yPFQggh3JzqPxTVvBXm63GV\nfsqeFAshhHBzSinUmIch14b+dG2l+pBiIYQQtYDy9MR4eCp627/Qu5Mr3F6KhRBC1BKqXn2Mh6di\nvrUC/evPFWorxUIIIWoR1bwVKuo+zJXPo8/kXXY7KRZCCFHLGDf0Q4V2xXxtyWUPeLtEsdi7dy+T\nJ09m0qRJrF9fc/O1CyFEbaGi7oXTp9AbL2/A2+mLhWmavPrqq0ybNo1Fixbx1Vdf8euvvzo6LCGE\ncGnKwxPjoafQmz9Hp6WWu77TF4uMjAwaNWpEUFAQHh4e9O7dm5SUFEeHJYQQLk/5B2A89ETp/RdZ\nRy65rtMXC5vNRmBgoP11QEAANpvNgREJIYT7UCHtUX+5E3Pl3Euu5/TFQgghRM1SkUNQ17S45Doe\nVyiWSgsICCA7O9v+2mazERAQUGad9PR00tPT7a+joqJo3LhxjcZltVprtH9Hk/xcm+Tn2hyS3/QF\nAKxd+78B79DQUEJDQ0tfaCdXUlKiJ0yYoLOysnRRUZF+/PHH9S+//FJuu/fee69C26nI+s8880yN\n9FtT61Z0fVfLr6KfheRX83FIfpVbV2vnyO9CnP7IwjAM7rvvPubMmYPWmv79+9O0adNy29mr4WWq\nyPpBQUE10m9NrVvR9V0tv4p+FpJfzcch+VVuXXCO/C5EaV1Nz9yrRdauXUtUVJSjw6gxkp9rk/xc\nm7PmJwPclVDVCu3sJD/XJvm5NmfNT44shBBClEuOLIQQQpRLioUQQohyOf3VUFdCTk4Oy5cvJzc3\nF6UUAwYMYMiQIeTl5bFkyRJOnDhBcHAwMTEx+Pj4kJeXx6JFi/jpp5+IjIzk3nvvtfe1fft2EhIS\nME2Tbt26cffddzsws1IVze8///kPb7/9NiUlJXh4eDBmzBg6dOhQps/58+dz4sQJFi5c6KCs/qc6\n83OH/ZeRkcHLL79sb3/bbbfRo0ePMn268v67VH7usP/Oys7O5rHHHiMqKophw4aV6dMh+69KF966\nid9++00fOHBAa611fn6+njhxoj58+LB+66239Pr167XWWickJOg1a9ZorbUuKCjQ+/bt01988YV+\n9dVX7f2cOnVKP/zww/rUqVNaa61XrFih09LSrmwyF1DR/A4cOKB/++03rbXWP//8s37ooYfK9Ldz\n504dFxenp0yZcuWSuITqys9d9t8ff/yhS0pK7G3vv/9++2utXX//XSw/d9l/Zy1atEgvXrxYf/zx\nx2Xed9T+k9NQgL+/P82bNwfA29ubJk2akJOTQ2pqKn379gUgMjLSPoFhnTp1aNOmDR4eZQ/Mjh8/\nTqNGjfDz8wOgQ4cO7Ny588olchEVza958+b4+/sD0KxZM4qKiiguLgagoKCATz/9lNGjR1/5RC6i\nuvJzl/3n5eWFYZT+1y4sLEQpZe/LHfbfxfJzl/0HkJKSQnBwMM2aNSvTlyP3n5yG+pOsrCwOHTpE\n69atyc3Ntf9S8ff3Jzc395JtGzZsyJEjR8jOzqZ+/fqkpKRQUlJyJcK+bBXNb8eOHVx33XX2wvje\ne+/xl7/8BS8vrysa9+WqSn7utP8yMjJYtWoV2dnZTJgwwf7L1V3234Xyc5f9V1BQwIYNG5g+fTob\nNmwo096R+0+KxTkKCgpYvHgx0dHReHt7n7f83G9oF+Lr68sDDzxAbGwshmHQunVrjh8/XlPhVlhF\n8/vll194++23mT59OgAHDx7k+PHj/O1vfyMrKwvtZFddVzU/d9p/ISEhLFq0iCNHjrB8+XK6du3K\n4cOH3Wb/XSg/d9l/77//PkOHDqVOnTplljv6/58Ui/8qKSlh0aJFRERE0L17d6C02p88edL+d716\n9crt5/rrr+f6668H4Msvv7R/o3O0iuaXk5PDwoULmTBhAsHBwQDs37+fzMxMJkyYQElJCbm5ucya\nNYuZM2c6JKdzVUd+4D7776zGjRvj7e3Nzz//TEZGhtvsv7POza9FixZusf8yMjLYuXMna9as4fTp\n0xiGgaenJ0opx+6/KzpC4sSWLVum33jjjTLvvfXWWzohIUFrfeEBqKSkpDID3FprnZubq7UuHSx9\n4okn9NGjR2sw6stXkfzy8vL0448/rnfu3HnR/rKyspxmgFTr6svPHfbf8ePH7QPAWVlZ+qGHHrIP\n+p7lyvvvUvm5w/4719q1a88b4NbaMftPjiyAffv2sXXrVq655hqefPJJlFLcddddjBgxgtjYWJKS\nkggKCiImJsbe5pFHHqGgoIDi4mJSUlKYPn06TZo04fXXX+fQoUMopbjtttto2LChAzMrVdH8Pv/8\nc44fP86HH37IBx98gFKKadOmcdVVVzk4kwurzvzcYf/t27ePjz76CA8PD5RS3H///fZBX2dUnfm5\nw/5zVjLdhxBCiHI5xwk9IYQQTk2KhRBCiHJJsRBCCFEuKRZCCCHKJcVCCCFEuaRYCCGEKJcUCyGE\nEOWSm/KEW3jkkUfIzc3FYrFQp04dunTpwn333Xfe/DrOaNOmTSQmJjJ79uxy1125ciXbtm3D09MT\ngKCgIK6//npGjBhR5lkIl/LII4/w8MMPn/eMEiEuRY4shNv4+9//zurVq5k/fz4//fQTH374oaND\numzlTVJ5rltvvZXVq1fz6quvMn78eH788UdmzJhBYWFhDUYoajvLs88++6yjgxCiqjZu3Ej37t0J\nDg6mbt26nDhxgsOHD1NcXExcXBzvvPMOiYmJeHl50aJFCwCmTJlCUFAQjRo1Akone3vggQfo3Lkz\nRUVF3HvvvQQFBTF//nzWrVtn/+Y+d+5c3nnnHWw2G127drXHkJiYyNKlS1m7di3p6em0a9fO3uaO\nO+7A39/fvjwnJ4frr7+eX3/9lfnz55Odnc2GDRvYsGEDI0aMuGieKSkp+Pj40KFDBwzDoH79+oSH\nh7Nhwwbq1q1LSEgIx48fZ/Hixbz55pt8/PHHHDx4kI4dO+Lp6cny5cvZt28fO3bs4KOPPsJisdCm\nTRv279/PkiVLeOutt9i+fTtNmjQhKCiopnaXcEFyZCHcTnZ2Nnv27OG6666jXr16TJ06ldWrVzN+\n/HhWr17NwYMHAejbty9btmyxt9u9ezcBAQH2B9VA6Qygy5YtY/LkybzxxhskJCTwzDPPsGjRIpKT\nk/n++++B0l/iH330EU888QTx8fG0bduWuLi4MnHt3r2befPmsWDBApKTk/nmm29o0qQJDzzwAK1b\nt+bNN9/k9ddfr3C+3t7edOzY0R6L1pqRI0fy8ssvExsbi81m4/333wdgwoQJNGjQgKeeeorVq1cz\nfPhwbDYb8+fPZ/To0bz++uvcc889LFq0iFOnTlU4FuG+pFgIt7FgwQLGjh3LzJkzCQ0NZeTIkXTt\n2tU+BXm7du3o1KmT/Zdqnz592LNnDwUFBQBs3bqVPn36lOnztttuw8PDg06dOuHt7U3v3r2xWq0E\nBATQtm1bDhw4AJROhz1ixAgaN26MYRiMGDGCgwcPkp2dbe9r5MiR1K1blwYNGhAaGmovWtUhICCA\n06dPA6UP4erYsSMWiwWr1crQoUP57rvvLtp269atdO3alS5dugDQsWNHWrRowZ49e6otPuH6ZIBb\nuI0nnnjivEHbPXv28MEHH3D06FG01hQWFnLttdcCUL9+fdq2bcuOHTvo0aMHe/bsYezYsWXanzvT\nrpeXV5lnKnh5edkLzYkTJ3jjjTd48803y7S32Ww0aNAAoEzbOnXq2NtWB5vNhq+vLwC5ubm8/vrr\n7Nu3j4KCAkzTvOSssydOnCA5OZldu3bZ3yspKZEBcFGGFAvhtoqLi1m8eDGPPvooYWFhGIbBggUL\nyjxhLCIigsTEREpKSmjTpg3169ev1LYCAwMZNWoUN954Y4XbVmRw+0IKCgpIS0uzP5f5nXfewTAM\nFi9ejI+PDykpKbz22msX3V6DBg3o27cvDz74YJXiEO5NTkMJt1VcXExxcTFWqxXDMNizZw//+c9/\nyqzTvXt3MjMz+X//7/8RERFR6W3ddNNNJCQkcPjwYQDOnDnDjh07LqttvXr1yMnJobi4uELbLC4u\nJjMzkwULFuDn50dkZCQA+fn5eHt74+3tjc1mO+85zv7+/mUeN9qnTx927drFN998g2maFBYW8t13\n32Gz2SoUj3BvcmQh3MKFvp17e3szduxYFi9eTHFxMd26dSMsLKzMOl5eXvTs2ZPt27fTs2fPSm+z\nR48e/PHHHyxZsoTs7Gx8fHzo1KkT4eHh5fbToUMHmjZtyoMPPohhGMTHx19y/Q0bNrBx40ag9Kig\nW7dujBgxAi8vLwBuv/12li9fztixY2nYsCF9+vTh008/tbcfMWIEr732GmvWrGH06NEMGzaMJ554\ngjVr1hAXF4fFYqFly5Y88MADFfo8hHuThx+JWu+DDz7g2LFjTJgwwdGhCOG05DSUqNXy8vJISkpi\n4MCBjg5FCKcmp6FErfXvf/+bN954g759+9K2bVtHh2M3ZcqUMpfcaq1RSvHAAw9UagBdiOogp6GE\nEOdri7sAAAAxSURBVEKUS05DCSGEKJcUCyGEEOWSYiGEEKJcUiyEEEKUS4qFEEKIckmxEEIIUa7/\nDyFl3zW8IkI0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55e4879f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedule1.plot(x='Payment_Date', y='Curr_Balance', title=\"Pay Off Timeline\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lanRPVQhXN7RHnkIbNRn5y3/R57yBPGu51DWKUl4qWNQS13R4p1Xv\nXAkPFwOTYoLRJUyLTyW/SAWMqhKNQtHGzUa07YQ+81X071Yii9UCVYr1qGBRi7gbNe7o5sHubdXb\n4Q3gYtB49c5A6nk4M/GXk1xWs72rTBgMaP0GoY2fgzycjD7j72qYrWI1KljUMr4W6vAGMGiCl7o2\noLW/kfE/nyQzT832rg6iXn200ZMRMQPQ57yB/p9/qSVdlRqngkUtZKkObyjtHxkW4Ud0kzqM+/mk\nWqq1mggh0O68C23i/NI06NNfQZ5IsXaxlFpEBYtayJId3mXHfzjcl8GtfRj/80mOXyio9nPUVqKu\nL9pLExD9B6PPn4L+zT+RReouQ7E8p6rsnJeXx3vvvcepU6cQQvD8888TEBDAvHnzyMjIwN/fn9jY\nWIxGIwBxcXGsW7cOg8HAsGHDaN++PQBHjx5l8eLFFBUVERERwbBhwwAoLi7m3Xff5ejRo5hMJmJj\nY6lXr17VaqwA/+vw3vjzZbx9DfjVd672c9zdrC4ezgYmrj3FuOhgWvpVfWEm5fclXSNjkK06oH+2\nBPnmaLRhIxFNW1i7aIoDq9Kdxccff0xERARz587lnXfeISgoiFWrVtG2bVvmz59PeHg4cXFxAKSm\nppKQkMDcuXMZO3Ysy5YtMzeBLFu2jOHDhzN//nzS0tLYvXs3AGvXrsXT05MFCxYwYMAAVqxYUcXq\nKldzN2pERBrZtTWP/DzLjGDq0bgOoyIDeGt9KrvSci1yjtpKeNVFe34s4r7H0BdNR//qY2ShShmi\nWEalg0VeXh4HDx4kJiYGKF031mg0kpSURM+ePQHo1asXiYmJACQlJREVFYXBYMDf35+AgABSUlLI\nzs4mPz+fsLAwAKKjo837JCYmmo8VGRnJvn37Kl9T5Yb86jvTpJkrO7bkopdYZgJYxyBPxkYHMXfz\nGTafuGSRc9RWQgi0zneiTV4ImefQp71M8ZFD1i6W4oAqHSzOnTuHyWRi8eLFjBkzhqVLl3LlyhUu\nXryIt7c3AN7e3ly8WJq2ICsr65omJB8fH7KyssjKysLX19f8uK+vL1lZWeZ9yp7TNA0PD48bLjKu\nVE1YK1dcXAUH9uRb7Byt/I1M6RPCsh3nVD4pCxAmL7ThYxAD/0Tu22PR//O5mpehVKtKBwtd1zl2\n7Bj9+/dn1qxZuLq6smrVquu2E0JUqYBXU6kPLEMIQYeuRs6eKeb0ScuNXmpS143pfRvy1f5MvknO\ntNh5ajOtSzSmGUuRRw+iz3wNmZZq7SIpDqLSHdw+Pj74+voSGhoKlDYTrVq1Cm9vb7Kzs83/e3l5\nmbc/f/68ef/MzEx8fHzw8fEhMzPzusfL9in7Xdd18vPz8fT0vK4sycnJJCcnm38fMmQIJpOpslW7\nLRcXF4se31p69nNn7f9l0LARFqtfCxMsHGzite8OcQUDz3YNrtYvFOXhqO9fGRcXX8SEf1C45lsK\n3nkd18FP4HL3gwjNMQY/Ov77Z936rVy50vxzeHg44eHhQBWChbe3N76+vpw5c4bAwED27dtHcHAw\nwcHBxMfHM2jQIOLj4+nUqRMAnTp1YsGCBQwcOJCsrCzS09MJCwtDCIHRaCQlJYXQ0FA2bNjAPffc\nY95n/fr1NGvWjISEBNq0aXPDslxdoTKXL1turQWTyWTR41uLkwu0bOvK2v9Lo3sfD5ycLXMRdwOm\n9Qlh6rpTZOcW8LfO9dFqMGA46vtXxmQylTbXRsYgmrYg/6N55G/bgPbkaISvn7WLV2W14f2zVv1M\nJhNDhgy54XNCVqFt5/jx4yxdupTi4mLq16/PCy+8gK7rzJ07l/Pnz+Pn50dsbCweHh5A6dDZtWvX\n4uTkdN3Q2UWLFpmHzj755JMAFBUVsXDhQo4fP47JZGLUqFH4+/uXq2xnzlgu+Zqjf1gP7C4iP6+Q\nO7oZLfqtP6+ohGnxqfh5ODMyMgCDVjMBw9Hfvz/WT+olyB9XIX+KQzzyJKJb7xq/m6tOte39q0mB\ngYE3fa5KwcKWqWBReUZ3T35YnUZwYxeaNne16LmuFOu8teE07k4ar3QPxNlg+YuYo79/N6ufPHUM\n/aO54NcA7S8vITzrWKF0VVdb37+acKtg4RiNmEq1MjgJOkUZ+e1AAVkZlh1R4+qkMaFnELqUzNiQ\nypVilbHWUkRIE7Rx/0DUq48+dTTy1z3WLpJiR1SwUG7I6GmgQxcjOxJyuVJg2Qu4s0HjtR5BeLgY\neFOlOLco4eyMNuRptL+OQP9obuna3yopoVIOKlgoN1U/0JmQJi7sSMhDWngVPCdNMLpbAA08nZm0\n9hQ5hSrFuSWJ8IjSpIRnTqLPHINMP23tIik2TgUL5ZZahLshgMMHLJ8M0KAJXuzagOa+bkxYc5KL\nBWpSmSUJk1dpUsLufdBnjUHf9LOay6TclAoWyi0JTRARaeTk0UIy0i3fXCGE4OmO/nQM9GT8GrUm\nhqUJIdBiBqD9fTpyzX/Ql85C5qosCcr1VLBQbsvNXSOiq5Fd2/IoyLd8f4IQgic6+NGrsRfjfj7J\nuRwVMCxNBDVCG/8PhLcv+tRRyEP7rV0kxcaoYKGUS736zjQKdWXnVsv3X5R5uI0vA1vUZdzPJzh9\nSS2iZGnC2QXt0WfR/vwC+gfvoH/7BVJXfUdKKRUslHJr3tq1xvovytzX0oc/ta3HhDUnOZGt0m/X\nBNG2I9qEOciDe9HnTUZevGDtIik2QAULpdzK+i9OHKmZ/osyd4V58+Qd/kz85SQpmWrVvZogvH3R\nXn4TEdoSfVqsmpOhqGChVIyb++8LJtVQ/0WZ6MZ1eL5LA6bGn+LweculUlf+RxgMaA8MRXtyNPqH\nc9FX/0s1S9ViKlgoFeZX35lGoS412n8BEBliYkTXAKbFp3IwQwWMmiJadyhtlvotGX3ORGS2Si9f\nG6lgoVRK89ZuQM32XwB0DvZkdFTpMq3J5/Jq9Ny1mfD2QXt5KqJ5G/RpLyOTd1m7SEoNU8FCqRSh\nCe4o6784W7NDW+8I9OTl7oHM2nCafWfVut41RWgGtPsfQ3v6ZfRP5qPHrUCWqGap2kIFC6XSzP0X\nW2u2/wKgQ4AHr/YI5J2NZ9idpgJGTRKt2qO9MRd57BD63InIS2qZ3NpABQulSvzqO9OwqQu7tuXV\neKqItvU9GBMdxJzNZ9h5Rs06rkmiTl200ZN/Hy31MvLIQWsXSbEwFSyUKmse7kZJieTIwZqfBxHu\nb2RszyDmbUkj6bQKGDVJaAa0wU+gPf439EXT0dd9p3JLOTAVLJQq0zTBHZEeHDl0hQuZNZ/8r5Wf\nkQm9glmwNY1tpxx3URxbJTp0RXt9FnL9D8iP5iKvqLkwjkgFC6VaGD002nZ0Z2dCHkVFNf/tsnk9\ndyb2CmHx9nS2nLxU4+ev7YR/INrY2QDoM15FnrXcSpWKdahgoVSbwBAX6tV3Yl9SzfdfAIT5ujEp\nJoT3E8+y8bgKGDVNuLoinopF9LwHfdYY5O6t1i6SUo1UsFCqVXiEOxezS0g9bp1MsU193JjcO4QP\nd5wl/thFq5ShNitNeX4v2ovj0T9/H/2b5Wp4rYNQwUKpVk5Ogo7dPDiwJ5+cy9a5SDSu68bUPg35\nZFcGa4+qgGENIrQl2oS5yOMp6AumIHNVX5K9U8FCqXZ1vA20CHdjx5Y8SkqsMzqmobcr0/qE8Onu\nDNapgGEVwuSFNmoyIqgR+vRXkKdPWrtIShWoYKFYRKMwF9w9BAf3Wm9kTLCXK1P7hLB8d4ZqkrIS\nYTCgDXkaMfBR9NnjkLtUP4a9UsFCsQghBB06GzmTWsjZM9Zb6S7k94Dxya4M1quAYTVaVG+0kZNK\n+zG+/QKp1+yMf6XqVLBQLMbFVSOiqwd7Ems+HcjVGnq5MqV3CB/vPMcGNUrKakSTZmjjZiOTd6K/\nNxNZoBJB2hMVLBSLqufvRKNQ66QDuVojb1em9GnIRzvOsi5Fpdi2FuHtg/bKdISHCX3mGGRGurWL\npJSTChaKxTVr7UZJseTYYesui9rI25XJvUNYtOUUm0+oOwxrEc7OiL+8hIjuXzqB78BuaxdJKQcV\nLBSL035fjvW3X69wKdu6Y+4b13Vj1r3NeT/pLJvVTG+rEUKg9R6I9tyr6B/OQV+zWuWVsnEqWCg1\nwsPTQKt2buzcmmu14bRlQusZzTO9VWoQ6xIt26G9/jZy0xrkisXI4prPLaaUjwoWSo0JaeKCp8lg\n1eG0ZZr6lKYGeS/xLAkq+aBVCb8GaGNmIS9k/j6BT2UPtkUqWCg1RghBu07unDlVSEa69YbTlikL\nGEu2p6tstVYm3I1oL40vncA341XkOZWI0NaoYKHUKBdXjQ5djOzenkfhFeuPtQ/1cWNirxAWbU9n\nW6oKGNYkNAPan55B9L0ffdbryEP7rV0k5SoqWCg1zq+BM4EhLuxNyreJTs0wXzfe6BXMoq3pasU9\nG6D1ugft6Vj0pbPQN6+xdnGU36lgoVhFy3Zu5Fwu4dSxQmsXBYBmvu6M7RnE3C1p7E1Xa3pbm2gd\ngfbqDOR3K9G/Xq5mfNsAFSwUqzAYSlfX+3VvAbk5tpHCupWfkTE9gnhn0xkOnFOzi61NBASjjZ2N\nPPJr6YxvtQKfValgoVhNHW8DzVq5smtrHrpu/eYogDb1jbzcPZCZG05z+Hy+tYtT6wlTHbTYNxFu\n7uizxyMvXbB2kWotIavYaKzrOmPHjsXHx4cxY8aQk5PDvHnzyMjIwN/fn9jYWIxGIwBxcXGsW7cO\ng8HAsGHDaN++PQBHjx5l8eLFFBUVERERwbBhwwAoLi7m3Xff5ejRo5hMJmJjY6lXr165ynXmjOVG\nU5hMJi5fdtzO0Jqsn5SSretz8annRIs2bjVyzvLUb3vqZd7dls7kmBCa+tRMuaqLI34+pZTI/3yO\n3BaPaews8kx1rV0ki7Hm+xcYGHjT56p8Z/H9998TFBRk/n3VqlW0bduW+fPnEx4eTlxcHACpqakk\nJCQwd+5cxo4dy7Jly8ydm8uWLWP48OHMnz+ftLQ0du8unf6/du1aPD09WbBgAQMGDGDFihVVLa5i\nY4QQRHQ1cjzlChcybWdCVpdgE8M712fqulOczLZumhLl9xnfDzyOuOdhcqaMRqYcsHaRap0qBYvM\nzEx27dpFnz59zI8lJSXRs2dPAHr16kViYqL58aioKAwGA/7+/gQEBJCSkkJ2djb5+fmEhYUBEB0d\nbd4nMTHRfKzIyEj27dtXleIqNsrNXaPtHe7s2pZHSbFtNEcBRDWsw5N3+DNp7SlOX7KNjvjaTuvR\nD+MLr6MvnoHcsdnaxalVqhQsli9fzhNPPIEQwvzYxYsX8fb2BsDb25uLF0vXEMjKyrqmCcnHx4es\nrCyysrLw9fU1P+7r60tWVpZ5n7LnNE3Dw8ODnBw1tNERBTZ0wbuugV/32lY/Qc8mXgxtX4+Jv5wk\n/bIKGLbAuX0XtNGT0b9Yhv7TKpsYfl0bVDpY7Ny5Ey8vLxo3bnzLN+vqQFJV6kPh2Nrc4U5aahHn\nz1p/dvfV+oZ681C4L2/8coqMXNsqW20lGoaW5pTavAb55TKkbhsj6hyZU2V3PHjwIElJSezatYvC\nwkLy8/NZuHAh3t7eZGdnm//38vICSu8kzp8/b94/MzMTHx8ffHx8yMzMvO7xsn3Kftd1nfz8fDw9\nPa8rS3JyMsnJyebfhwwZgslkqmzVbsvFxcWix7c2q9XPBJHRLiRuvsA9D3rj4mKZwXqVqd+fOprQ\nnFyYtDaVuQ+0pJ6Hi0XKVh1qzefTZEJ/cxF5cyYils3B+NI4hKt9DUa4EWu/fytXrjT/HB4eTnh4\nOFCFYPH444/z+OOPA3DgwAG+/fZbRowYwYoVK4iPj2fQoEHEx8fTqVMnADp16sSCBQsYOHAgWVlZ\npKenExYWhhACo9FISkoKoaGhbNiwgXvuuce8z/r162nWrBkJCQm0adPmhmW5ukJlLDmawBFHm1zN\nmvUzeUO9+ga2bcigQ1ejZc5Ryfrd3dSDy3kmXl79K9Pvaoi3W6X/fCyqtn0+5UsTkMsXcnFqLNqI\nNxAe9h0orfr3ZzIxZMiQGz5X7V/dBg0axL59+xg1ahT79+9n0KBBAAQHB9OtWzdiY2OZMWMGzzzz\njLmJ6umnn2bJkiWMGjWKBg0a0KFDBwB69+7NpUuXGDlyJN9//705OCmOrXV7dzIzikk/bXtNPo+0\nqUdUQxOTfjlFzhXV9GELhJMz4snRiNBWpTmlsjKsXSSHVOV5FrZKzbOoPFuoX2ZGMTu25NKzvwlX\nt+r9TlPV+kkp+WjnOQ6dz2dK74a4O9vW3FZbeP8s6Vb103+MQ679L9royYiAkBouWfVw2HkWimIJ\nvn5OBDd2Ye8O20g2eDUhBE/d4U+IlytvbUilsETlLbIVWv/BiAeGls72PnLQ2sVxKCpYKDarRRs3\nci+XcPqE7TVHCSF4oUsDTC4GZm86Q7GNpCtRQIvqjTZsJPq705D7dli7OA5DBQvFZhkMpbO7k3fn\nk59ne9/eDZogNiqQYl2yMCEN3cbugGoz0bYT2ksT0D+Zj751nbWL4xBUsFBsmlddJ5o0c2X39jyb\na44CcDYIxvQI4lxuEe8nnrXJMtZWIrQl2ivTkHGfov+0ytrFsXsqWCg2L6yVK8VFkhMptjmD2tVJ\n442YYA5nFvDpbjUSx5aIwIal63tv/An93x+rYF4FKlgoNk/TBB26Gjm4v4A8G1n74o+MzgYmxwSz\nLTWHfydn3n4HpcYIHz+0MTORh5ORny5Ss70rSQULxS6Y6hgIa+XK7kTbGx1Vpo6bE1P7hPBTSjb/\nd1itu2BLhGcdtJenIs+lIT+ciyy2nQzH9kIFC8VuhDZ3paRYcuKIbTZHAfganZnaO4Sv9mcSf+yi\ntYujXEW4GdFGTkTm55WuvFdku58jW6SChWI3RFlz1L4C8nJttymhgcmFyb1D+HjnObadctzJcfZI\nuLiivTAO4eKKvmAqssC2shzbMhUsFLtiqmMgrKUre7bbbnMUQENvVyb0CmbRtnT2pOdauzjKVYST\nE+KZlxF+DdDnTkTmqmUPykMFC8XuNG3hSrGNN0cBNPN1Z0yPIGZvOsMhtZ63TRGaAfHEi4imLX9f\n2zvb2kWyeSpYKHZH0wQduhg5tN+2m6MAwusbGRkZwFvrUzl1US3PakuEEIghTyEiItHfHqsSEN6G\nChaKXTJ5GWjawpU9Njw6qkznYE/+GuHPlLVq8SRbI4RAu/8xRHT/0oBxznIJSO2dChaK3Qpt4UpR\noeTkUdtujgLo3dSLgS3rMnntKS6p1OY2R+s3CHHvI+izJyDTU61dHJukgoVit8qao37dW0Beru3l\njk85eMgAAB1nSURBVPqjQa186RLsyZvrTpFfZPvlrW206P6lGWv/MQF55qS1i2NzVLBQ7FodbwOh\nLVzZk2ibuaP+6C8d/AjxcmXWxtMUldh+eWsbrXsfxEN/RZ8zEZl63NrFsSkqWCh2L7Sl/TRHCSF4\nsWsDnA2CBSpTrU3SImMQf3oafd4k5Mmj1i6OzVDBQrF7VzdH2WIq8z8yaIK/dw/kfF4Ry3acs4s7\notpG69wD7bG/oc+fjDyRYu3i2AQVLBSHUMfbQJNmLuzbYR/NUa5OGuN7BZN8No+v9qvEg7ZIdIxC\ne+IF9PlTkMcOW7s4VqeCheIwwlq5kZujc+aUfQxP9XQxMKl3CL8cvcgPv6nEg7ZIdIhE++tI9IVv\nIlN+tXZxrEoFC8VhGAyCDp2NJO/Kp/CK7TdHAfi4OzG5dwhf7Mtk88lL1i6OcgOifWe0p0ajL34L\neTjZ2sWxGhUsFIdSt54TgSHOJO+yn/QaASYXJvYKZun2s+xVeaRskmjTEe2ZV0qz1f52wNrFsQoV\nLBSH07KtO5nnSziXZh/NUQBNfdx4tUcg72w6w5GsAmsXR7kB0boD2jMvoy+ZgTxy0NrFqXEqWCgO\nx8lZ0K6TO3uT8igusv3O7jJt63vwQpcGTItP5WyO7Q8Dro1E6wi0J0ejL5pe6zq9VbBQHJJ/A2d8\n/Z04uM9+mqMAujU08XC4L5PXpnKpQK3mZotE247/6/SuRcNqVbBQHFZ4B3fOnCoi67x9XXQHtKhL\ntxBPpq1P5UqxfXTU1zaifWe0J14sHVZbSybuqWChOCwXV402Ee7sScyjxM5SazzRwY8ATxdmbz5D\niW5fZa8tREQk2tDnSyfu1YLUICpYKA4tIMQZD0+NlF/tq9NYCMFLkQFcKdZZmnjWLiYa1kaiYxTi\n0WfR5012+OSDKlgoDk0IQduORo6nFHL5on2lBnc2CF6PDuJwZj5fJatZ3rZK69wD8fCw0iVa0xw3\nvbkKForDczdqtGjjxu7teUg7a9IxOhuYGBPCzykX+eWIWvrTVmmRvRCDn0Cf8wbyrGMuoKSChVIr\nNAp1QTPAsRT7G5Lq4+7EpJhg/rk7g51ncqxdHOUmtKg+iPseLb3DcMAlWlWwUGoFIQTtOhn57UAB\nebn2NToKINjLldd7BDF3SxopmfbV/1KbaNH9Eb0Hlq6Hccmx8n2pYKHUGqY6BhqHuZC0xT6bc1r5\nG3mhawOm/X97dx4fVXU2cPx370wmk2XIHsgCAkYWE/ZVkYS6vG9f0DYWTKu2HwFBi8QFES1CpVqt\npphAJGpr2eGVj4gGYrWvrxYQlKUBwmIwSghbAmQbmRLIkEzmvn/kzZjIEhISZubm+f4Dk7nn3vNw\nwjxzzzn3nC+KOX3W++6QOgr1P5JRhifiXDAP7Zx+7gQlWYgOJa6vGduZWk6XeM9SII3d0tXCffFh\nvLjphDy058GUe36F0ndA/bRa+3l3V6dNSLIQHYrBoDB8VAgH9njXUiCNjesdwq3dOvHHzfLQnqdS\nFAXlvskosd1xZr2CVnPB3VW6ZpIsRIfTOdpMRKQPBV97b9//rweEE93JRLo8tOexFEVB+fU0lKAQ\nnH9JQ3N4952gorXyaZ/KykqysrKw2WwoisIdd9zB2LFjqaqqYuHChZSXlxMZGcmMGTPw9/cHIDs7\nm02bNmEwGJg4cSIDBgwAoKioiLfeeova2loGDRrExIkTAXA4HGRlZVFUVITFYmHGjBmEh4dfVf1O\nnmy/6WsWi4WzZ8+22/ndrSPEV1FhY/M/zjIiMYDgUKO7q9QqtXUaL246QfcQX6YM6ez6eUdoP2+K\nT3M4cP7lNRSTL8qUp1FUwxWPd2d80dHRl32v1XcWBoOBhx56iIyMDF555RU+/fRTSkpKWL9+Pf36\n9SMzM5P4+Hiys7MBKC4uZvv27SxYsIDZs2ezePFi11Opixcv5re//S2ZmZmcOnWKvXv3ArBx40YC\nAwN54403GDduHKtXr25tdYVowtdX5eYBfuzfVY3TS7+Z+xgUfjc6hryT5/j7t1Z3V0dchmI0oj76\nLNpZG9rKN9Gc3tl12OpkERwcTPfu3QEwm83ExMRQWVnJrl27SEpKAmDMmDHk5uYCsGvXLm699VYM\nBgORkZFERUVRWFjImTNnqK6uJi4uDoDExERXmdzcXNe5Ro4cyYEDB1odqBA/FtvdB6OPwtFD3tuf\nHOhr4IWfxLIu38q/ir3n23ZHo/iYUKfPQTt1Au39ZV65fEubjFmUlZVx7NgxevXqhc1mIzg4GKhP\nKDabDQCr1dqkCyk0NBSr1YrVaiUsLMz187CwMKxWq6tMw3uqqhIQEEBVlX6mogn3qn/2wo/vDl7g\n/Dnv/LYH0DnQxPOJMWTtOC3PYHgwxeyH+sQ8tG/2ov3PB+6uTotdc2et3W4nIyODiRMnYjabL3pf\nUZRrvYTL5bJxfn4++fk/7I2bkpKCxWJps+v+mMlkatfzu1tHis9igT4JCgX7a0i8K7xNf1+vpyEW\nCzPx4U9bjvHXLmGEdpD28zoWC84586ma9wSmiM74/mTsRYe4O761a9e6/h4fH098fDxwjcmirq6O\n9PR0EhMTGTZsGFB/N3HmzBnXn0FBQUD9nURFRYWrbGVlJaGhoYSGhlJZWXnRzxvKNLx2Op1UV1cT\nGBh4UT0aB9SgPQeIvG2AraU6WnxdeyocOVTDoQIrUbEmN9bs2gwIN5LcN4RnNhzkT3fGEmC68kCq\nt/L6308fMzwxj+rXn+eC0YQycESTt90Zn8ViISUl5ZLvXVM31Ntvv01sbCxjx/6QHYcMGcLmzZsB\n2Lx5M0OHDgVg6NChbNu2DYfDQVlZGadPnyYuLo7g4GD8/f0pLCxE0zS2bNniSjxDhw7liy++AGD7\n9u0kJCRcS3WFuCSDoX4pkK/3VFPrpc9eNLindwgDYyykbS3B4aUD9x2B0iUGdfpcnCuz0L7Lb76A\nB2j11NmCggLmzZtHt27d6ucTKwr3338/cXFxLFiwgIqKCiIiIpgxYwYBAQFA/dTZjRs3YjQaL5o6\n++abb7qmzk6aNAmA2tpaFi1axNGjR7FYLDz55JNERkZeVf1k6mzrddT49v7rPEYjJAz2d0Ot2o5/\nQCDPf1xAkNlA6oguXtu1djl6+v3UDubhXJyB+vRLKLE9AM+dOtvqZOHpJFm0XkeNr+aCk01e/uwF\n1MdXZrUx5/NjjOxqISXh6p5N8hZ6+/105m5FW7sU9dlXUSK6eGyykCe4hfh/Jl+Vvv3NHNhd7ZVT\nGxvz81GZO6YrnxWe4YsjNndXR1yBOmw0yn+Nr99t79+eu8ilJAshGunaw4SiwPEi71/VNdTPyNwx\nXVmyu4z8Un0sZqdX6u13owwfjfONl9CqPbOtJFkI0Uj9Nqx+FBywc+GC9z570eCGYF+eHhXNn78s\n4ZQsa+7RlJ89gHLDjZxL/z2aw/NWRZZkIcSPBIUYienmQ8E+fTzgNjAqgPv7h/PHzcVUXfCufcg7\nEkVRUB78LYqfP9qKRR7XFSrJQohL6J3gR9npWqwV3r1SaIOf3hTC4OgA/vylTKn1ZIpqwD91DlrZ\nKbT1/+3u6jQhyUKIS/AxKdw8wI8Du8977UKDPzZpUCRGVeFvu0o97lur+IHia0ZNnYu2ayvOLf/j\n7uq4SLIQ4jKiu/ngY1I5WqiPvn6DqvDMbdF8U1bN37/V1/7QeqNYglCfnIeWswbtwC53VweQZCHE\nZTUMdh86aMde7f2D3QD+PgbmjInhg4NWdpXIopyeTImMRp02G+fShWjHCt1dHUkWQlyJpZOBbj1N\nHNxb7e6qtJnOgSaeGx3NG9tPceyM9y7P3hEoN/ZB/c10nFkvo1WUurUukiyEaMZNN5uxVjioKPW8\n6Yyt1TfCn8lDInl5czFn7PoYxNcrZfAtKD+dgDPzRbRz7ntyXZKFEM0wGhUSBvtzYHc1zjr9DAyP\n6RHEmB6d+NMXJdTU6aObTa/UO+5G6T8U55uvoNW6ZwxNkoUQV6FztBH/QJXD3+mr2+b+/uGE+xvJ\n2nFaZkh5OGX8RJROIWhLF7pla1ZJFkJcBUVRSBjsx+EC795V78dUReHJW6I4ebaG97+ubL6AcBtF\nVVEenoF2xoqWveq6X1+ShRBXKSDQQI+bTBzcp5/BbgBfo8rzSbF8WniGL4/9293VEVeg+JhQH3se\nbfdXOL/6/LpeW5KFEC0Q18fMmUp9DXZD/aKDc5Ji+WtuKYcq9ZUM9UaxdEJ9/AW0D1agffv1dbuu\nJAshWsBgVIgf5MfXe6p182R3g56hZh4b3oXXtpTwfbXMkPJkSlQs6pSZOP+ahlbafnv3NCbJQogW\n6hLjg6+ffp7sbuyWbhbuujGYV7eUUCszpDyacvNAlOQHcS7643WZUivJQogWahjsPnTQzgW7/j5Q\nU/qFEepn5K1/yRpSnk5N/Gn9lNq3X0NztO/doCQLIVrB0slA7A0mvtmvj2XMG2uYIVVktfORrCHl\n8ZQJE8Hsh/buX9o1uUuyEKKVeiWYKTtVy/eV+uvf9/NReT4phg/zK8k7dc7d1RFXoKgG1Ckz0Y4c\nQvtsfbtdR5KFEK3k46PQt3/9YLceu2s6B5qYdVsMC7ad5OS/9Tc+oyeK2Q/18blon21A27ujXa4h\nyUKIaxDb3QdFgRNH9PlhGt/Znwf6h/PKF8Wcq5Fd9jyZEhqB+tgcnCuy0I4fbvPzS7IQ4ho0DHYX\nHLBTW6O/wW6o32WvX2d/Mr46SZ3OpgvrjdLjJtRfT6tfQ8rWtuNNkiyEuEbBoUY6R/vw7df6G+xu\nMGVoZ+x1Gqv3lbu7KqIZypBRKKPuxPn2q2i1bffwqCQLIdpAn/5mSo7X8u8z+uyqMaoKz90WzVfH\nz7L5iM3d1RHNUO7+FXQKRlvz1zYbT5NkIUQb8PVV6RVv5us8fQ52A3QyG3k+MYYlu8tkSRAPp6gq\n6uQZaEXfom3+pE3OKclCiDZyw40mai84OXVCX+tGNdY9xMz0EV14dUsJVlkSxKMpZj/U6XPQ/v4e\nWsH+az6fJAsh2oiqKsQP9ufgvmrqHPq8uwAY2dXCf8QFk7alhFodbQalR0pEl/o1pP72Olr56Ws6\nlyQLIdpQeKSR4FCj7jZJ+rGUhDCCzAb+tsu9+0KL5il9B6CMTamfIWVvffehJAsh2ljfAWaKvr2A\nvVqfU2mhfkmQp26NIr/sPP9beMbd1RHNUG4fh9L9JpzLMlu9y54kCyHaWECggRtuNPHNfn0PAvv7\nGJidFMPqveUUlOs7Vm+nKArKg9PAZkX7eG2rziHJQoh2cFNfM+WnHbpcN6qx2E6+PD4yij9vLaHy\nvH4H9vVA8fFBnTYb7cv/RduzvcXlJVkI0Q6MPgp9+pnJ1/FU2gbDYgP5z5uCSdt6UvbA8HBKUAjq\ntNk4V72JVnK8RWUlWQjRTrr2MOF0wsnj+v/GfV9CGMFmA3/bVebuqohmKN1vQkl5GOdbf0I7X3XV\n5SRZCNFOFKV+C9aD+6tx6HgqLTQd8P70kAx4ezr1lp+gxA/CuXThVQ94e0Wy2Lt3L0899RRPPvkk\n69e333rtQrS1sAgjoWFGDhfoeyot1A94P58Uy3/vK+eb8vPuro5ohpIyGc6dRfvk6ga8PT5ZOJ1O\nlixZwpw5c0hPT+err76ipKTE3dUS4qr1HeDHkUMXqD6v//78mE4mnrglivlbT8qAt4dTjD6ojz6H\n9sWnaAd2NXu8xyeLwsJCoqKiiIiIwGg0MmrUKHJzc91dLSGumn+ASvc4/U+lbTA0JpCfyoC3V1CC\nQ1EfnVX//EXZySse6/HJwmq1EhYW5nodGhqK1Wp1Y42EaLm4PmYqyxx8X6HvqbQNJiSEEeInA97e\nQIm7GeWeX+F869UrHufxyUIIPaifSuun61VpG1MVhSdvieJg+Xk+kye8PZ4yZixKt55XPMZ4nerS\naqGhoVRUVLheW61WQkNDmxyTn59Pfn6+63VKSgrR0dHtWi+LxdKu53c3ia/tRUfDiFHX51qe0n4f\nPtK1Xc7rKfG1F7fEN3c+AGvX/jDgHR8fT3x8fP0LzcPV1dVpqampWllZmVZbW6s988wz2okTJ5ot\n995777XoOi05/oUXXmiX87bXsS093tvia+m/hcTX/vWQ+Fp3rKZ5RnyX4vF3Fqqq8vDDD/Pyyy+j\naRq33347sbGxzZZzZcOr1JLjIyIi2uW87XVsS4/3tvha+m8h8bV/PSS+1h0LnhHfpSia1gE6UNvY\n2rVrSUlJcXc12o3E590kPu/mqfHJAHcrXGuG9nQSn3eT+Lybp8YndxZCCCGaJXcWQgghmiXJQggh\nRLM8fjbU9VBZWUlWVhY2mw1FUbjjjjsYO3YsVVVVLFy4kPLyciIjI5kxYwb+/v5UVVWRnp7O4cOH\nGTNmDJMnT3ada9u2bWRnZ+N0OhkyZAgPPPCAGyOr19L49u/fz7vvvktdXR1Go5EHH3yQhISEJudM\nS0ujvLyc119/3U1R/aAt49ND+xUWFvLOO++4yk+YMIHhw4c3Oac3t9+V4tND+zWoqKjg6aefJiUl\nhbvvvrvJOd3Sftc08VYnvv/+e+3IkSOapmladXW19sQTT2jFxcXaqlWrtPXr12uapmnZ2dna6tWr\nNU3TNLvdrhUUFGifffaZtmTJEtd5zp49q02bNk07e/aspmma9uabb2oHDhy4vsFcQkvjO3LkiPb9\n999rmqZpx48f1x599NEm59u5c6eWmZmpzZw58/oFcQVtFZ9e2u/ChQtaXV2dq+yUKVNcrzXN+9vv\ncvHppf0apKenaxkZGdpHH33U5Ofuaj/phgKCg4Pp3r07AGazmZiYGCorK9m1axdJSUkAjBkzxrWA\noa+vL71798ZobHpjVlpaSlRUFIGBgQAkJCSwc+fO6xfIZbQ0vu7duxMcHAxA165dqa2txeGoX9PI\nbrfz8ccfM378+OsfyGW0VXx6aT+TyYSq1v/XrqmpQVEU17n00H6Xi08v7QeQm5tLZGQkXbs2fQLe\nne0n3VA/UlZWxrFjx+jVqxc2m831oRIcHIzNZrti2S5dunDy5EkqKioICQkhNzeXurq661Htq9bS\n+Hbs2EGPHj1cifG9997jnnvuwWQyXdd6X61riU9P7VdYWMjbb79NRUUFqamprg9XvbTfpeLTS/vZ\n7XZycnKYO3cuOTk5Tcq7s/0kWTRit9vJyMhg4sSJmM3mi95v/A3tUgICApg6dSoLFixAVVV69epF\naWlpe1W3xVoa34kTJ3j33XeZO3cuAEePHqW0tJSHHnqIsrIyj1sQ71rj01P7xcXFkZ6ezsmTJ8nK\nymLQoEEUFxfrpv0uFZ9e2u/9999n3Lhx+Pr6Nnnf3f//JFn8v7q6OtLT00lMTGTYsGFAfbY/c+aM\n68+goKBmzzN48GAGDx4MwOeff+76RuduLY2vsrKS119/ndTUVCIjIwH47rvvKCoqIjU1lbq6Omw2\nGy+++CLz5s1zS0yNtUV8oJ/2axAdHY3ZbOb48eMUFhbqpv0aNI6vZ8+eumi/wsJCdu7cyerVqzl3\n7hyqquLj44OiKO5tv+s6QuLBFi1apC1fvrzJz1atWqVlZ2drmnbpAahNmzY1GeDWNE2z2WyaptUP\nls6aNUs7depUO9b66rUkvqqqKu2ZZ57Rdu7cednzlZWVecwAqaa1XXx6aL/S0lLXAHBZWZn26KOP\nugZ9G3hz+10pPj20X2Nr1669aIBb09zTfnJnARQUFLB161a6devGs88+i6Io3H///SQnJ7NgwQI2\nbdpEREQEM2bMcJWZPn06drsdh8NBbm4uc+fOJSYmhmXLlnHs2DEURWHChAl06dLFjZHVa2l8n376\nKaWlpXzwwQesW7cORVGYM2cOnTp1cnMkl9aW8emh/QoKCtiwYQNGoxFFUZgyZYpr0NcTtWV8emg/\nTyXLfQghhGiWZ3ToCSGE8GiSLIQQQjRLkoUQQohmSbIQQgjRLEkWQgghmiXJQgghRLMkWQghhGiW\nPJQndGH69OnYbDYMBgO+vr4MHDiQhx9++KL1dTzR5s2b2bhxIy+99FKzx7711lt8+eWX+Pj4ABAR\nEcHgwYNJTk5ushfClUyfPp1p06ZdtEeJEFcidxZCN373u9+xYsUK0tLSOHz4MB988IG7q3TVmluk\nsrGf//znrFixgiVLlvDYY49x6NAhfv/731NTU9OONRQdneEPf/jDH9xdCSGu1SeffMKwYcOIjIzE\nz8+P8vJyiouLcTgcZGZmsmbNGjZu3IjJZKJnz54AzJw5k4iICKKiooD6xd6mTp3KgAEDqK2tZfLk\nyURERJCWlsaHH37o+ub+6quvsmbNGqxWK4MGDXLVYePGjbzxxhusXbuW/Px8+vbt6yrzy1/+kuDg\nYNf7lZWVDB48mJKSEtLS0qioqCAnJ4ecnBySk5MvG2dubi7+/v4kJCSgqiohISGMHDmSnJwc/Pz8\niIuLo7S0lIyMDFauXMlHH33E0aNH6devHz4+PmRlZVFQUMCOHTvYsGEDBoOB3r17891337Fw4UJW\nrVrFtm3biImJISIior2aS3ghubMQulNRUUFeXh49evQgKCiI2bNns2LFCh577DFWrFjB0aNHAUhK\nSmLLli2ucnv27CE0NNS1UQ3UrwC6aNEinnrqKZYvX052djYvvPAC6enpbN++nW+++Qao/xDfsGED\ns2bNYvHixfTp04fMzMwm9dqzZw+vvfYa8+fPZ/v27ezbt4+YmBimTp1Kr169WLlyJcuWLWtxvGaz\nmX79+rnqomka9957L++88w4LFizAarXy/vvvA5Camkp4eDjPPfccK1as4Gc/+xlWq5W0tDTGjx/P\nsmXL+M1vfkN6ejpnz55tcV2EfkmyELoxf/58Jk2axLx584iPj+fee+9l0KBBriXI+/btS//+/V0f\nqqNHjyYvLw+73Q7A1q1bGT16dJNzTpgwAaPRSP/+/TGbzYwaNQqLxUJoaCh9+vThyJEjQP1y2MnJ\nyURHR6OqKsnJyRw9epSKigrXue699178/PwIDw8nPj7elbTaQmhoKOfOnQPqN+Hq168fBoMBi8XC\nuHHjOHjw4GXLbt26lUGDBjFw4EAA+vXrR8+ePcnLy2uz+gnvJwPcQjdmzZp10aBtXl4e69at49Sp\nU2iaRk1NDTfccAMAISEh9OnThx07djB8+HDy8vKYNGlSk/KNV9o1mUxN9lQwmUyuRFNeXs7y5ctZ\nuXJlk/JWq5Xw8HCAJmV9fX1dZduC1WolICAAAJvNxrJlyygoKMBut+N0Oq+46mx5eTnbt29n9+7d\nrp/V1dXJALhoQpKF0C2Hw0FGRgaPP/44Q4cORVVV5s+f32SHscTERDZu3EhdXR29e/cmJCSkVdcK\nCwvjF7/4BbfddluLy7ZkcPtS7HY7Bw4ccO3LvGbNGlRVJSMjA39/f3Jzc1m6dOllrxceHk5SUhKP\nPPLINdVD6Jt0QwndcjgcOBwOLBYLqqqSl5fH/v37mxwzbNgwioqK+Mc//kFiYmKrr3XXXXeRnZ1N\ncXExAOfPn2fHjh1XVTYoKIjKykocDkeLrulwOCgqKmL+/PkEBgYyZswYAKqrqzGbzZjNZqxW60X7\nOAcHBzfZbnT06NHs3r2bffv24XQ6qamp4eDBg1it1hbVR+ib3FkIXbjUt3Oz2cykSZPIyMjA4XAw\nZMgQhg4d2uQYk8nEiBEj2LZtGyNGjGj1NYcPH86FCxdYuHAhFRUV+Pv7079/f0aOHNnseRISEoiN\njeWRRx5BVVUWL158xeNzcnL45JNPgPq7giFDhpCcnIzJZALgvvvuIysri0mTJtGlSxdGjx7Nxx9/\n7CqfnJzM0qVLWb16NePHj+fuu+9m1qxZrF69mszMTAwGAzfeeCNTp05t0b+H0DfZ/Eh0eOvWreP0\n6dOkpqa6uypCeCzphhIdWlVVFZs2beLOO+90d1WE8GjSDSU6rH/+858sX76cpKQk+vTp4+7quMyc\nObPJlFtN01AUhalTp7ZqAF2ItiDdUEIIIZol3VBCCCGaJclCCCFEsyRZCCGEaJYkCyGEEM2SZCGE\nEKJZkiyEEEI06/8A2IXB1liVV+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55e4729ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "schedule1.plot(x='Payment_Date', y='Curr_Balance', label=\"Scenario 1\", ax=ax)\n",
    "schedule2.plot(x='Payment_Date', y='Curr_Balance', label=\"Scenario 2\", ax=ax)\n",
    "schedule3.plot(x='Payment_Date', y='Curr_Balance', label=\"Scenario 3\", ax=ax)\n",
    "plt.title(\"Pay Off Timelines\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lwKZ+X2BuuGLnaNtWVavV8tRTTzFhwgSWLl2KzWYDuOC2qklJSRw4cEDdVlWn07XbVrVN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\nEEII0SEJCyGEEB36/wGMtKa6PM6z5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55e46d6048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedule1[\"Cum_Interest\"] = schedule1[\"Interest\"].abs().cumsum()\n",
    "schedule2[\"Cum_Interest\"] = schedule2[\"Interest\"].abs().cumsum()\n",
    "schedule3[\"Cum_Interest\"] = schedule3[\"Interest\"].abs().cumsum()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "\n",
    "schedule1.plot(x='Payment_Date', y='Cum_Interest', label=\"Scenario 1\", ax=ax)\n",
    "schedule2.plot(x='Payment_Date', y='Cum_Interest', label=\"Scenario 2\", ax=ax, style='+')\n",
    "schedule3.plot(x='Payment_Date', y='Cum_Interest', label=\"Scenario 3\", ax=ax)\n",
    "\n",
    "ax.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1CtXV1RBCICgoCPfddx+6deuG2NhYJCQkwN3dHVFRUQAAT09PBAYGIioqCra2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55e46db668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "y1_schedule = schedule1.set_index('Payment_Date').resample(\"A\")[\"Interest\"].sum().abs().reset_index()\n",
    "y1_schedule[\"Year\"] = y1_schedule[\"Payment_Date\"].dt.year\n",
    "y1_schedule.plot(kind=\"bar\", x=\"Year\", y=\"Interest\", ax=ax, label=\"30 Years @ 5%\")\n",
    "\n",
    "plt.title(\"Interest Payments\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
